Diabetes mellitus is a leading cause of vision loss in industrialized and developed countries. Diabetic retinopathy (DR) is a common complication of type 1 diabetes, affecting up to 60% of individuals with type 1 diabetes with a duration of ≧15 years at any point in time. The Diabetes Control and Complications Trial (DCCT) demonstrated that 10% of patients with good metabolic control (glycated hemoglobin ≦6.87%) developed retinopathy, whereas 43% of patients with poor metabolic control (glycated hemoglobin ≧9.49%) did not develop retinopathy. These data suggest that although poor glycemic control is an important predictor of retinopathy, there are many individuals who develop retinopathy despite good glycemic control. Identifying additional predictors of retinopathy is therefore important in screening for the development of this complication.
In diseases in which diagnosis is based mainly on an image, as in DR, not only the contribution of new imaging technologies is essential, but also the development of quantitative computed-assisted tools to aid in the diagnoses is fundamental for the establishment of methods in clinical practice to assist physicians and for improving the quality of medical care. Even though, detailed and well-documented diagnostic protocols have been developed over the last 20 years, there are still constrains in the underlying data generation mechanisms found in actual diabetic screening. Image quality has also been identified as a limited factor.